An Exploration of Stress: Leveraging Online Data from Crowdsourcing Platforms - Roddy, James; Robinson, Samantha (Frontiers in Artificial Intelligence, 2021)
Background: Early detection of community health risk factors such as stress is of great interest to health policy makers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms. Methods: An online sample of Amazon Mechanical Turk (MTurk) workers (N = 500) filled out, for themselves and their child, demographic information and the 10-item Perceived Stress Scale (PSS-10), designed to measure the degree to which situations in one's life are appraised as stressful. Internal consistency reliability of the PSS-10 was examined via Cronbach’s alpha. Analysis of Variance (ANOVA) was utilized to explore trends in the average perceived stress of both adults and their children. Lastly, Rasch Trees were utilized to detect Differential Item Functioning (DIF) in the set of PSS-10 items. Results: The PSS-10 showed adequate internal consistency reliability (Cronbach’s alpha = 0.73). ANOVA results suggested that stress scores significantly differed by Education (p = 0.024), Employment Status (p = 0.0004), and Social Media Usage (p = 0.015). Rasch Trees, a recursive partitioning technique based on the Rasch model, indicated that items on the PSS-10 displayed DIF attributable to Physical Health for adults and Social Media Usage for children. Conclusions: The key conclusion is that this data collection scheme shows promise, allowing public health officials to examine health risk factors such as perceived stress quickly and cost-effectively.